Cardiac mapping system and method for voltage-based evaluation of electrograms

ABSTRACT

Systems and methods for evaluating electrograms are described. An example method of evaluating an electrogram such as an atrial and/or ventricular electrogram containing a plurality of data samples each having a voltage includes selecting an activity interval for the electrogram, calculating an energy level for each window of a plurality of windows of the electrogram based on the voltages of the data samples in each window, assigning the calculated energy levels to a plurality of bins, and calculating an index based at least in part on a number of energy levels assigned to a particular bin of the plurality of bins.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/971,054 filed on May 4, 2018, which is a Continuation of U.S. patentapplication Ser. No. 14/527,015 filed on Oct. 29, 2014, now issued U.S.Pat. No. 9,990,470, which claims priority to U.S. Provisional PatentApplication No. 61/897,597, filed Oct. 30, 2013, the entire contents ofwhich are incorporated by reference herein for all purposes.

BACKGROUND OF THE DISCLOSURE A. Field of the Disclosure

The present disclosure relates generally to an electrophysiology systemand method used to measure electrical activity occurring in the heart ofa patient and to visualize the electrical activity and/or informationrelated to the electrical activity. More particularly, the presentdisclosure relates to processing of data to detect and evaluate complexfractionated electrograms and the use of such data in three-dimensionalmapping of the electrical activity associated with complex fractionatedelectrograms.

B. Background Art

The heart contains two specialized types of cardiac muscle cells. Themajority, around ninety-nine percent, of the cardiac muscle cells iscontractile cells, which are responsible for the mechanical work ofpumping the heart. Autorhythmic cells comprise the second type ofcardiac muscle cells, which function as part of the autonomic nervoussystem to initiate and conduct action potentials responsible for thecontraction of the contractile cells. The cardiac muscle displays apacemaker activity, in which membranes of cardiac muscle cells slowlydepolarize between action potentials until a threshold is reached, atwhich time the membranes fire or produce an action potential. Thiscontrasts with a nerve or skeletal muscle cell, which displays amembrane that remains at a constant resting potential unless stimulated.The action potentials, generated by the autorhythmic cardiac musclecells, spread throughout the heart triggering rhythmic beating withoutany nervous stimulation.

The specialized autorhythmic cells of cardiac muscle comprising theconduction system serve two main functions. First, they generateperiodic impulses that cause rhythmical contraction of the heart muscle.Second, they conduct the periodic impulses rapidly throughout the heart.When this system works properly, the atria contract about one sixth of asecond ahead of ventricular contraction. This allows extra filling ofthe ventricles before they pump the blood through the lungs andvasculature. The system also allows all portions of the ventricles tocontract almost simultaneously. This is essential for effective pressuregeneration in the ventricular chambers. The rates at which theseautorhythmic cells generate action potentials differ due to differencesin their rates of slow depolarization to threshold in order to assurethe rhythmical beating of the heart.

Normal autorhythmic cardiac function may be altered by neuralactivation. The medulla, located in the brainstem above the spinal cord,receives sensory input from different systemic and central receptors(e.g., baroreceptors and chemoreceptors) as well as signals from otherbrain regions (e.g., the hypothalamus). Autonomic outflow from thebrainstem is divided principally into sympathetic and parasympathetic(vagal) branches. Efferent fibers of these autonomic nerves travel tothe heart and blood vessels where they modulate the activity of thesetarget organs. The heart is innervated by sympathetic and vagal fibers.Sympathetic efferent nerves are present throughout the atria (especiallyin the sinoatrial node) and ventricles, including the conduction systemof the heart. The right vagus nerve primarily innervates the sinoatrialnode, whereas the left vagus nerve innervates the atrial-ventricularnode; however, there can be significant overlap in the anatomicaldistribution. Efferent vagal nerves also innervate atrial muscle.However, efferent vagal nerves only sparsely innervate the ventricularmyocardium. Sympathetic stimulation increases heart rate and conductionvelocity, whereas parasympathetic (vagal) stimulation of the heart hasopposite effects.

An arrhythmia occurs when the cardiac rhythm becomes irregular, i.e.,too fast (tachycardia) or too slow (bradycardia), or the frequency ofthe atrial and ventricular beats are different. Arrhythmias can developfrom either altered impulse formation or altered impulse conduction. Theformer concerns changes in rhythm that are caused by changes in thepacemaker cells resulting in irregularity or by abnormal generation ofaction potentials by sites other than the sinoatrial node, i.e., ectopicfoci. Altered impulse conduction is usually associated with complete orpartial blockage of electrical conduction within the heart. Alteredimpulse conduction commonly results in reentry, which can lead totachyarrhythmias. Reentry can take place within a small local region orit can occur, for example, between the atria and ventricles (globalreentry). Reentry requires the presence of a unidirectional block withina conducting pathway usually caused by partial depolarization of thepacemaker cells. Arrhythmias can be either benign or more serious innature depending on the hemodynamic consequences of arrhythmias andtheir potential for changing into lethal arrhythmias.

Electrophysiology studies may be used to identify and treat thesearrhythmias. In one exemplary system, a measurement system introduces amodulated electric field into the heart chamber. The blood volume andthe moving heart wall surface modify the applied electric field.Electrode sites within the heart chamber passively monitor themodifications to the field and a dynamic representation of the locationof the interior wall of the heart is developed for display to thephysician. Electrophysiology signals generated by the heart itself arealso measured at electrode sites within the heart and these signals arelow pass filtered and displayed along with the dynamic wallrepresentation. This composite dynamic electrophysiology map may bedisplayed and used to diagnose the underlying arrhythmia.

In addition to mapping for diagnosis, the measurement system can also beused to physically locate a therapy catheter in a heart chamber. Amodulated electrical field delivered to an electrode on this therapycatheter can be used to show the location of the therapy catheter withinthe heart. The therapy catheter location can be displayed on the dynamicelectrophysiology map in real time along with the other diagnosticinformation. Thus the therapy catheter location can be displayed alongwith the intrinsic or provoked electrical activity of the heart to showthe relative position of the therapy catheter tip to the electricalactivity originating within the heart itself. Consequently, thephysician can guide the therapy catheter to any desired location withinthe heart with reference to the dynamic electrophysiology map.

The dynamic electrophysiology map is generally produced in a step-wiseprocess. First, the interior shape of the heart is determined. Thisinformation is derived from a sequence of geometric measurements relatedto the modulation of the applied electric field. Knowledge of thedynamic shape of the heart is used to generate a representation of theinterior or exterior surface of the heart. Next, the intrinsicelectrical activity of the heart is measured. The signals of physiologicorigin are passively detected and processed such that the magnitude ofthe potentials on the wall surface may be displayed on the wall surfacerepresentation. The measured electrical activity is displayed on thewall surface representation in any of a variety of formats, for example,in various colors or shades of a color. Finally, a location current maybe delivered to a therapy catheter within the same chamber. Thepotential sensed from this current may be processed to determine therelative or absolute location of the therapy catheter within thechamber. These various processes occur sequentially or simultaneouslyseveral hundred times a second to give a continuous image of heartactivity and the location of the therapy device.

If ablation is the indicated therapy, then a therapy catheter ispositioned at the desired location within the heart and energy isdelivered to the therapy catheter to ablate the tissue. The use ofcomplex fractionated atrial electrograms (CFAEs) has become one toolused to identify atrial fibrillation ablation sites. For example, in onemethod, utilized in the EnSite™ Velocity™ mapping system available fromSt. Jude Medical, a set of activation events are recognized in the CFAEsignal, and then time intervals between subsequent activation events arecalculated. The average time interval is determined and designated asthe CFE mean. Locations whose cycle length is shorter than apredetermined threshold (e.g., 120 milliseconds (ms)) are identified aspotential ablation sites.

Other known systems use various other metrics to detect, characterize,and/or evaluate CFAEs. For example, some systems use a CFE standarddeviation (CFE StdDev) by detecting activations and computing thestandard deviation of the cycle length between successive detectedactivations. In other systems, the shortest interval between activationdetections is used as an index (sometimes referred to as ShortestComplex Interval (SCI)) for the investigation of CFAEs, while othersystems use an average of all CFAE complex intervals (ACI) in a signal.An Interval Confidence Level (ICL) is used by some other systems. TheICL is the number of intervals during a recording period that have alength between 70 ms and 120 ms. Some systems utilize a frequency basedmetric, such as a Dominant Frequency (DF) metric. In systems using theDF metric, the time based electrogram is transformed into frequencyspace and the most dominant frequency component in the transformedelectrogram is identified as the DF.

The various known cycle length based metrics, e.g., CFE mean, CFEStdDev, SCI, ACI, and ICL, used in evaluation of CFAEs depend onaccurate activation detection results. Activation detection results arehighly dependent on parameter settings. Tuning to the proper parametersettings can be difficult and time consuming. Moreover, if a signal hasvarying properties, it may be very difficult to find optimal parametersthat are applicable to the entire signal. It is thus desirable toprovide accurate, useful metric(s) for CFAE analysis that areinsensitive to activation detection.

BRIEF SUMMARY OF THE DISCLOSURE

In one embodiment, a computer implemented method for evaluating anelectrogram containing a plurality of data samples each having a voltageis described. The computer implemented method includes selecting anactivity interval for the electrogram, calculating an energy level foreach window of a plurality of windows of the electrogram based on thevoltages of the data samples in each window, assigning the calculatedenergy levels to a plurality of bins, and calculating an index based atleast in part on a number of energy levels assigned to a particular binof the plurality of bins.

In another embodiment, a system for evaluating an electrogram containinga plurality of data samples each having a voltage includes a computingdevice configured to receive the data samples is described. Thecomputing device includes a processor and at least one memory devicecoupled to the processor. The memory device stores computer-executableinstructions that, when executed by the processor, cause the computingdevice to: calculate an energy level for each window of a plurality ofwindows of the electrogram based on the voltages of the data samples ineach window, assign the calculated energy levels to a plurality of bins,and calculate an index based at least in part on a number of energylevels assigned to a particular bin of the plurality of bins.

The foregoing and other aspects, features, details, utilities andadvantages of the present disclosure will be apparent from reading thefollowing description and claims, and from reviewing the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for performing a cardiacelectrophysiology examination or ablation procedure wherein the locationof one or more electrodes can be determined and recorded.

FIG. 2 is a schematic representation of a heart investigated by anelectrophysiology catheter with several distal electrodes.

FIG. 3 is a schematic block diagram of a computing device for use in thesystem shown in FIG. 1.

FIG. 4 is a flow diagram of an exemplary method for evaluating anelectrogram segment.

FIG. 5 is an example electrogram segment length for evaluation using themethod shown in FIG. 4.

FIG. 6 is an example histogram produced using the method shown in FIG.4.

FIG. 7 is an example electrogram with a relatively high degree offractionation and a histogram for the electrogram.

FIG. 8 is an example electrogram with a relatively low degree offractionation and a histogram for the electrogram.

FIG. 9 is a flow diagram of an exemplary method for determining thenumber of bins for use in a method of calculating a voltage-basedisoelectric index.

FIG. 10 is a graph of the results of a receiver operating characteristic(ROC) analysis.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION OF THE DISCLOSURE

The present disclosure relates generally to mapping systems and methodsfor mapping anatomic structures, such as the human heart or portionsthereof, and more particularly to the processing of data from atrialelectrograms—such as complex fractionated atrial electrograms (CFAEs)and the use of such data in the mapping system. In particularembodiments, the systems and methods of the present disclosure use avoltage based matrix for evaluation of CFAEs. While in the embodimentsherein the systems and methods are used for activation detection infractionated electrograms, it is contemplated that the systems andmethods disclosed herein may be used in non-fractionated electrograms aswell. Additionally, while the various embodiments herein are describedin connection with mapping of a patient's heart, it is understood thatthe present disclosure is not limited to mapping of a heart, and thatmapping of other anatomic structures is considered to be within thescope of the present disclosure.

Known systems and methods exist for generating a three-dimensional modelof an anatomic structure such as the heart, including systems thatutilize technology such as CT scanning, MRI, ultrasound imaging, radarimaging, x-ray imaging, and fluoroscopic imaging. The output of suchdata may be a plurality of x-y-z data coordinates, spherical coordinatesand/or other formats to provide a three-dimensional image. Such imagingtechnology is often useful in diagnosis as well as preparing for apatient's treatment and/or surgery. The imaging process may be performedhours or days before treatment and/or surgery, or concomitantly with thetreatment and/or surgery. Some three-dimensional models utilize asegmented approach, including for example a segmented CT or MRI scanimage. A segmented model indicates that a subregion of athree-dimensional image has been digitally separated from a largerthree-dimensional image, e.g., an image of the right atrium separatedfrom the rest of the heart. Other methodologies and techniques forcreating a three-dimensional model of a portion of the patient may alsobe utilized in accordance with the present disclosure.

Data acquired from the imaging process is typically used to partitionthe three-dimensional model into discrete surface elements to facilitatenumerical computation during subsequent mapping and reconstruction. Itis understood that various computational methods may be used topartition the three-dimensional model into discrete segments, such asfinite differences, Finite Element Methods (FEM) and Boundary ElementMethods (BEM) such as spline BEM or linear BEM. The three-dimensionalmodel of the anatomic structure generally includes a boundary surfacedefined by the discrete segments, with the boundary surface thusdefining an interior (broadly, a first side) of the three-dimensionalmodel and an exterior (broadly, a second side) of the three-dimensionalmodel of the anatomic structure.

With reference now to the drawings and in particular to FIGS. 1-3, oneexample of a system 8 is illustrated for conducting cardiacelectrophysiology studies by navigating a cardiac catheter into a heart10 of a patient 11 to measure electrical activity occurring in the heartand to three-dimensionally map the electrical activity and/orinformation related to or representative of the electrical activity. Thesystem 8 is particularly used to measure electrophysiology data at aplurality of points along an endocardial surface, and store the measureddata in association with location information for each measurement pointat which the electrophysiology data was measured. In one embodiment, forexample, the system 8 can instantaneously locate up to sixty-fourelectrodes in and/or around a heart and the vasculature of a patient,measure electrical activity at up to sixty-two of those sixty-fourelectrodes, and provide a three-dimensional map of time domain and/orfrequency domain information from the measured electrical activity(e.g., electrograms) for a single beat of the heart 10. The number ofelectrodes capable of being simultaneously monitored is limited only bythe number of electrode lead inputs into the system 8 and the processingspeed of the system 8. The electrodes may be stationary or may bemoving. In addition, the electrodes may be in direct contact with thewall of the heart, or may be merely generally adjacent to the wall ofthe heart, to collect the electrical activity. In another embodiment, anarray of electrodes is used for collecting electrical activity atmultiple locations along the wall of the heart. Such an array electrodeis described in detail in U.S. Pat. No. 5,662,108, which is herebyincorporated by reference herein in its entirety.

In one suitable embodiment, the localization/mapping system 8 may be theEnSite™ Velocity™ navigation and visualization system available from St.Jude Medical, Inc. In other embodiments, any other suitablelocalization/mapping system may be used.

The patient 11 is depicted schematically as an oval for simplicity.Three sets of surface electrodes (e.g., patch electrodes) are shownapplied to a surface of the patient 11 along an X-axis, a Y-axis, and aZ-axis. The X-axis surface electrodes 12, 14 are applied to the patientalong a first axis, such as on the lateral sides of the thorax region ofthe patient (e.g., applied to the patient's skin underneath each arm)and may be referred to as the Left and Right electrodes. The Y-axiselectrodes 18, 19 are applied to the patient along a second axisgenerally orthogonal to the X-axis, such as along the inner thigh andneck regions of the patient, and may be referred to as the Left Leg andNeck electrodes. The Z-axis electrodes 16, 22 are applied along a thirdaxis generally orthogonal to the X-axis and the Y-axis, such as alongthe sternum and spine of the patient in the thorax region and may bereferred to as the Chest and Back electrodes. The heart 10 lies betweenthese pairs of surface electrodes. An additional surface referenceelectrode (e.g., a “belly patch”) 21 provides a reference and/or groundelectrode for the system 8. The belly patch electrode 21 is analternative to a fixed intra-cardiac electrode 31. It should also beappreciated that in addition, the patient 11 will have most or all ofthe conventional electrocardiogram (ECG) system leads in place. This ECGinformation is available to the system 8 although not illustrated in theFIG. 1.

A representative catheter 13 having at least a single electrode 17(e.g., a distal electrode) is also shown. This representative catheterelectrode 17 is referred to as the “roving electrode” or “measurementelectrode” throughout the specification. Typically, multiple electrodeson the catheter will be used. In one embodiment, for example, the system8 may comprise up to sixty-four electrodes on up to twelve cathetersdisposed within the heart and/or vasculature of the patient. Of course,this embodiment is merely exemplary, and any number of electrodes andcatheters may be used within the scope of the present invention.

The fixed reference electrode 31 (e.g., attached to a wall of the heart10) is shown on a second catheter 29. For calibration purposes, thiselectrode 31 may be stationary (e.g., attached to or near the wall ofthe heart) or disposed in a fixed spatial relationship with the rovingelectrode 17. The fixed reference electrode 31 may be used in additionto or alternatively to, the surface reference electrode 21 describedabove. In many instances, a coronary sinus electrode or other fixedelectrode in the heart 10 can be used as a reference for measuringvoltages and displacements.

Each surface electrode is coupled to the multiplex switch 24, and thepairs of electrodes are selected by software running on a computer 20,which couples the electrodes to a signal generator 25. The computer 20,for example, may comprise a conventional general-purpose computer, aspecial-purpose computer, a distributed computer, or any other type ofcomputer. The computer 20 may comprise one or more processors, such as asingle central-processing unit, or a plurality of processing units,commonly referred to as a parallel processing environment. The signalgenerator 25 excites a pair of electrodes, for example the Y-axiselectrodes 18, 19, which generates an electric field in the body of thepatient 11 and the heart 10.

During the delivery of the current pulse, the remaining surfaceelectrodes are referenced to the surface electrode 21, and the voltagesinduced on these remaining electrodes are filtered via a low pass filter(LPF) 27. The LPF 27 may, for example, comprise an anti-aliasing filter(e.g., a 300 Hz analog LPF). The output of the LPF 27 is then providedto an analog-to-digital (A/D) converter 26 that converts the analogsignal to a digital data signal. Further low pass filtering of thedigital data signal may be subsequently performed by software executedon the computer 20 to remove electronic noise and cardiac motionartifact. This filtering may, for example, comprise a user-selectablecutoff frequency used to reduce noise. In this manner, the user cancustomize the system to trade off signal noise against signal fidelityaccording to the user's individual preferences. In this fashion, thesurface electrodes are divided into driven and non-driven electrodesets. A pair of surface electrodes (e.g., the X-axis electrodes 12, 14)are driven by the signal generator 25, and the remaining, non-drivensurface electrodes and other reference electrodes, if any, (e.g., theY-axis electrodes 18, 19, the Z-axis electrodes 16, 22, the surfacereference electrode 21, and, if present, the fixed reference electrode31) are used as references to synthesize the position of anyintracardial electrodes.

Generally, three nominally orthogonal electric fields are generated by aseries of driven and sensed electric dipoles in order to realizecatheter navigation in a biological conductor. Alternately, theseorthogonal fields can be decomposed and any pairs of surface electrodescan be driven as dipoles to provide effective electrode triangulation.Additionally, such nonorthogonal methodologies add to the flexibility ofthe system. For any desired axis, the potentials measured across anintra-cardiac electrode 17 resulting from a predetermined set of drive(source-sink) configurations are combined algebraically to yield thesame effective potential as would be obtained by simply driving auniform current along the orthogonal axes. Thus, any two of the surfaceelectrodes 12, 14, 16, 18, 19, 22 may be selected as a dipole source anddrain with respect to a ground reference, e.g., the belly patch 21,while the unexcited electrodes measure voltage with respect to theground reference. The measurement electrode 17 placed in the heart 10 isexposed to the field from a current pulse and its voltage is measuredwith respect to ground, e.g., with respect to the belly patch 21. Inpractice, the catheters within the heart may contain multipleelectrodes, and each electrode potential may be measured. As previouslynoted, at least one electrode may be fixed to the interior surface ofthe heart to form a fixed reference electrode 31, which is also measuredwith respect to ground. Data sets from each of the surface electrodes,the internal electrodes, and the virtual references are all used todetermine the location of the measurement electrode 17 or otherelectrodes within the heart 10.

All of the raw electrode voltage data is measured by the A/D converter26 and stored by the computer 20 under the direction of software. Thiselectrode excitation process occurs rapidly and sequentially asalternate sets of surface electrodes are selected and the remainingnon-driven electrodes are used to measure voltages. This collection ofvoltage measurements is referred to herein as the “electrode data set.”The software has access to each individual voltage measurement made ateach electrode during each excitation of each pair of surfaceelectrodes. The raw electrode data is used to determine the “base”location in three-dimensional space (X, Y, Z) of the electrodes insidethe heart, such as the roving electrode 17, and any number of otherelectrodes located in or around the heart and/or vasculature of thepatient 11. FIG. 2 shows a catheter 13, which may be a conventionalelectrophysiology (EP) catheter, extending into the heart 10. In FIG. 2,the catheter 13 extends into the left ventricle 50 of the heart 10. Thecatheter 13 comprises the distal electrode 17 discussed above withrespect to FIG. 1 and has additional electrodes 52, 54, and 56. Sinceeach of these electrodes lies within the patient (e.g., in the leftventricle of the heart in this example), location data may be collectedsimultaneously for each of the electrodes. In addition, when theelectrodes are disposed adjacent to the surface, although notnecessarily directly on the surface of the heart, and when the signalsource 25 is “off” (i.e., when none of the surface electrode pairs isenergized), at least one of the electrodes 17, 52, 54, and 56 can beused to measure electrical activity (e.g., voltage) on the surface ofthe heart 10.

In summary, the system 8 first selects a set of electrodes and thendrives them with current pulses. While the current pulses are beingdelivered, electrical activity, such as the voltages measured at leastone of the remaining surface electrodes and in vivo electrodes aremeasured and stored. At this point, compensation for artifacts, such asrespiration and/or impedance shifting may be performed as indicatedabove. As described above, various location data points are collected bythe system 8 that are associated with multiple electrode locations(e.g., endocardial electrode locations). Each point in the set hascoordinates in space. In one embodiment, the system 8 collects locationdata points for up to sixty-four electrodes that may be located on up totwelve catheters simultaneously or in close proximity to one another.However, smaller or larger data sets may be collected and result in lesscomplex and lower resolution or more complex and higher resolutionrepresentations of the heart, respectively.

The electrode data may also be used to create a respiration compensationvalue used to improve the raw location data for the electrode locationsas described in U.S. Pat. No. 7,263,397, which is hereby incorporatedherein by reference in its entirety. The electrode data may also be usedto compensate for changes in the impedance of the body of the patient asdescribed, for example, in U.S. Pat. No. 7,885,707, which is alsoincorporated herein by reference in its entirety.

The data used to determine the location of the electrode(s) within theheart are measured while the surface electrode pairs impress an electricfield on the heart. A number of electrode locations may be collected byeither sampling a number (e.g., sixty-two electrodes spread among up totwelve catheters) simultaneously or in sequence (e.g., multiplexed)and/or by sampling one or more electrodes (e.g., the roving electrode17) being moved within the patient (e.g., a chamber of the heart). Inone embodiment, the location data for individual electrodes are sampledsimultaneously, which allows for collection of data at a single stage orphase of a heartbeat. In another embodiment, location data may becollected either synchronously with one or more phases of the heartbeator without regard for any particular stage of the heartbeat. Where thedata is collected across the phases of the heartbeat, data correspondingto locations along the wall of the heart will vary with time. In onevariation, the data corresponding to the outer or inner locations may beused to determine the position of the heart wall at the maximum andminimum volumes, respectively. For example, by selecting the mostexterior points it is possible to create a “shell” representing theshape of the heart at its greatest volume.

A three-dimensional model of a portion of the patient, e.g., a region ofthe patient's heart or surrounding vasculature, may be created from thelocation data points, e.g., during the same or a previous procedure, ora previously generated three-dimensional model, e.g., a segmented CT orMRI scan image, may be used. A segmented model indicates that asubregion of a three-dimensional image has been digitally separated froma larger three-dimensional image, e.g., an image of the right atriumseparated from the rest of the heart. Exemplary segmentationapplications include ANALYZE (Mayo, Minneapolis, Minn.), Verismo™ (St.Jude Medical, Inc., St. Paul, Minn.), and CardEP (General ElectricMedical Systems, Milwaukee, Wis.). Where the three-dimensional model iscreated from the location data points collected by the system 8, forexample, during a single procedure, the exterior-most location points inthe data can be used to determine a shape corresponding to the volume ofa region of the patient's heart.

In one variation, for example, a convex hull may be generated usingstandard algorithms such as the Qhull algorithm. The Qhull algorithm,for example, is described in Barber, C. B., Dobkin, D. P., andHuhdanpaa, H. T., “The Quickhull algorithm for convex hulls,” ACM Trans.on Mathematical Software, 22(4):469-483, December 1996. Other algorithmsused to compute a convex hull shape are known and may also be suitablefor use in implementing the invention. This surface is then re-sampledover a more uniform grid and interpolated to give a reasonably smoothsurface stored as a three-dimensional model for presentation to thephysician during the same or a later procedure. Such a three-dimensionalmodel, for example, provides an estimated boundary of the interior ofthe heart region from the set of points.

FIG. 3 is a block diagram of the computer system 20. The computer system20 includes the computing device 32, the display device 23, and theinput device 38. The computing device 32 includes a display adapter 40communicatively coupling the computing device 32 to the display device23. Display device 23 may include, without limitation, a monitor, atelevision display, a plasma display, a liquid crystal display (LCD), adisplay based on light emitting diodes (LED), a display based on aplurality of organic light-emitting diodes (OLEDs), a display based onpolymer light-emitting diodes (PLEDs), a display based on a plurality ofsurface-conduction electron-emitters (SEDs), a display including aprojected and/or reflected image or any other suitable electronic deviceor display mechanism. In one embodiment, display device 23 includes atouch-screen with an associated touch-screen controller. An interfaceadapter 42 couples the computing device 32 to the input device 38.Computing device 32 includes an input 44 configured to receive electrodesignals through A/D converter 26. An output 46 couples control signalsfrom computing device 32 to multiplex switch 24. Input device 38includes, without limitation, a keyboard, a keypad, a touch-sensitivescreen, a mouse, a scroll wheel, a pointing device, an audio inputdevice employing speech-recognition software, and/or any suitable devicethat enables a user to input data into computing device 32. In someembodiments, input device 38 and display device 23 are integrated into asingle input/display device, such as in a touch screen display device.

The computing device 32 includes a processor 34 and a memory device 36coupled to the processor 34. The term “processor” refers hereingenerally to any programmable system including systems andmicrocontrollers, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), programmable logic circuits, fieldprogrammable gate array (FPGA), gate array logic (GAL), programmablearray logic (PAL), digital signal processor (DSP), and any other circuitor processor capable of executing the functions described herein. Theabove examples are exemplary only, and thus are not intended to limit inany way the definition and/or meaning of the term “processor.” Moreover,although a single processor is illustrated in FIG. 3, the processor 34may include more than one processor and the actions described herein maybe shared by more than one processor.

The memory device 36 stores program code and instructions, executable bythe processor 34. When executed by the processor 34, the program codeand instructions cause the processor 34 to operate as described herein.The memory device 36 may include, but is not limited to only include,non-volatile RAM (NVRAM), magnetic RAM (MRAM), ferroelectric RAM(FeRAM), read only memory (ROM), flash memory and/or ElectricallyErasable Programmable Read Only Memory (EEPROM). Any other suitablemagnetic, optical and/or semiconductor memory, by itself or incombination with other forms of memory, may be included in the memorydevice 36. The memory device 36 may also be, or include, a detachable orremovable memory, including, but not limited to, a suitable cartridge,disk, CD ROM, DVD or USB memory. Although illustrated separate from theprocessor 34, memory device 36 may be integrated with the processor 34.

The memory device 36 stores instructions (e.g., software code) that,when executed by the processor 34, cause the processor 34 to operate asdescribed above and in accordance with the methods set forth herein.

Various electrophysiology data may be measured and presented to acardiologist through the display 23 of the system 8 shown in FIG. 1. Thedisplay 23, for example, may be used to show data to a user, such as aphysician, and to present certain options that allow the user to tailorthe configuration of the system 8 for a particular use. The display mayinclude a three-dimensional model of the heart 10. The locations ofelectrodes on one or more catheters may be mapped to thethree-dimensional model. Other data that may be mapped to the heartsurface model include, for example, the magnitude of a measured voltage,the timing relationship of a signal with respect to heartbeat events.Further, the peak-to-peak voltage measured at a particular location onthe heart wall may be mapped to show areas of diminished conductivityand may reflect an infarct region of the heart.

In one embodiment, atrial electrogram information, and in a moreparticular embodiment complex fractionated electrogram (CFE)information, may be mapped to the three-dimensional model. In oneexample, such mapping of CFE information may be useful to identifyingand guiding ablation targets for atrial fibrillation. CFE informationrefers to irregular electrical activation (e.g., atrial fibrillation) inwhich an electrogram comprises at least two discrete deflections and/orperturbation of the baseline of the electrogram with continuousdeflection of a prolonged activation complex (e.g., over a 10 secondperiod). Electrograms having very fast and successive activations are,for example, consistent with myocardium having short refractory periodsand micro-reentry.

The presence of CFE information can be detected from theelectrophysiology (EP) information (e.g., electrograms) collected by anelectrode. For example, time instant and/or other quantifications of thefractionation of the electrogram may be used to determine the presenceand/or absence of CFE information. CFE information may be quantifiedusing one or more indices.

In an example embodiment, a voltage-based index is used to characterizethe fractionation of a given electrogram. The voltage-based index is avoltage-based isoelectric index (v-IEI). The value of v-IEI for aparticular electrogram is an indication of how much of the electrogramconsists of isoelectric portions. Less fractionated electrograms aretypically associated with a greater percentage of isoelectric portionsthan more fractionated electrograms.

FIG. 4 is a flow diagram of an exemplary method 400 for evaluating anelectrogram segment. The method 400 will be described with reference tothe system 8 and the computing device 20, but may be implemented usingany suitable electrophysiology devices and/or computing devices. FIG. 5is an example electrogram 500 having a segment length 502. Theelectrogram 500 is graphed as an amplitude, in millivolts (mV) as afunction of time, in seconds. In the illustrated example, the segmentlength 502 is about one second. The segment length 502 is variable andselectable by the user of the system 8, and the segment length 502 maybe any suitable length of time. In some embodiments, the segment lengthis between five seconds and eight seconds.

At 402 of the method illustrated in FIG. 4, an activity interval isselected. The activity interval is a period of time that is shorter thanthe segment length. The activity interval defines a temporal window inwhich cardiac activity is searched for, and is generally selected to belong enough to capture typical cardiac activity, but small enough toinclude only temporally local phenomena. The activity interval may beuser selected and/or a default activity interval may be predefined incomputing device 20. In an example embodiment, the activity interval isselected to be 10 milliseconds (ms) by default. In other embodiments,any other suitable activity interval may be defined as a defaultactivity interval. It should be understood that selecting the activityinterval may include selecting a default activity interval by notspecifying, changing, or selecting a different activity interval.

An energy level is calculated at 404 for a plurality of windows in theelectrogram. Each window has a length equal to the activity interval. Inan example embodiment, one window is defined for each sample (e.g., eachdiscrete electrode measurement recorded by the system 8) in theelectrogram, with the window beginning at the time of the sample andcontinuing for the length of the activity interval. Alternatively, thewindows may begin at any other suitable time. Moreover, in someembodiments, more or fewer windows are defined. In one exampleembodiment, the sampling rate of the system 8 is about 2,034 samples persecond. In other embodiments, the sampling rate of the system 8 is anyother sampling rate that provides satisfactory resolution of theelectrogram. In FIG. 5, three windows 504, 506 and 508 are shown, eachof which is defined by an activity interval 510. For explanatorypurposes, the windows 504, 506, and 508 are defined by an activityinterval 510 of about 90 ms. In other embodiments, the activity intervalmay be selected to be a shorter length of time, such as about 10 ms. Asdescribed above, in an example embodiment, one window is defined foreach sample in the electrogram segment. If the system 8 is sampling2,034 samples per second, the one second long electrogram 500 shown inFIG. 5 would have 2,034 samples and up to 2,034 windows. For clarity andsimplicity, only three windows 504, 506, and 508 of the 2,034 possiblewindows are shown in FIG. 5.

For each window in the electrogram, the energy level (also referred tosometimes as an activity level) is calculated by summing the absolutevalue of the measured amplitude of each sample in the window. Thecalculation may be represented by:

E(i)=Σ_(n=n) ₀ ^(n) ⁰ ^(+N−1) |V(n)|  (1)

where E(i) is the energy level of the i^(th) window, N is the number ofsamples in the window, n₀ is the first sample in the window, and V(n) isthe voltage of the n^(th) sample. In an example embodiment, the samplingrate of the system 8 is about 2,034 samples per second, the activityinterval is about 10 ms, and each window includes about twenty samples.After the energy level has been calculated for each window of thesegment, the system 8 will have M separate energy values, where M is thenumber of windows defined for the electrogram segment. As can be seen inFIG. 5, window 506, which includes a generally isoelectric portion ofthe electrogram segment with an amplitude around zero, will have a muchlower energy level E(i) than windows 504 and 508, which include multiplesmall deflections and a large deflection respectively.

With reference back to FIG. 4, the calculated energy levels are assignedto a plurality of bins at 406. The bins define a histogram with a rangefrom the minimum calculated energy level E(i) to the maximum calculatedenergy level E(i). The bins are each assigned a non-overlapping range ofpossible energy values between the minimum E(i) and the maximum E(i),such that every possible energy value between the minimum and themaximum values of E(i) are associated with a bin. The size of the rangeof each bin is determined by the number of bins, with the range ofcalculated E(i) values divided equally among the bins. In otherembodiments, the bins need not all have a same range size. In an exampleembodiment, the number of bins is a fixed, predetermined number of bins.In another embodiment, the number of bins is a user selectable number ofbins. In other embodiments, the number of bins is determined by thesystem 8, as will be described in more detail below.

The energy value E(i) for each window is assigned to the bin with theenergy range that encompasses the energy value E(i) of that window. Thesystem 8 tracks the number of windows assigned to each bin. This data isused to populate a histogram of the number of windows in each binordered from lowest energy bin to highest energy bin. An examplehistogram 600 with twenty bins is shown in FIG. 6. When organized fromlowest energy values to highest energy values as shown in FIG. 6, thefirst bin is the bin that includes the window(s) having the lowestenergy values, including the windows with an energy value E(i) equal tothe minimum energy value E(i). As mentioned above, low energy valuewindows are typically associated with isoelectric portions of theelectrogram. Thus, the lowest energy first bin B_(n)(1) is associatedwith isoelectric portions of the electrogram segment.

Referring again to FIG. 4, at 408 a voltage-based isoelectric index(v-IEI) is calculated based at least in part on the number of energyvalues E(i) assigned to a particular bin of the plurality of bins. Inthe exemplary embodiment, the particular bin is the bin with the lowestenergy values. When organized by increasing energy value, the particularbin is the first bin. The v-IEI is calculated as:

$\begin{matrix}{{v\text{-}{IEI}} = \frac{B_{n}(1)}{M}} & (2)\end{matrix}$

where B_(n)(1) is the number of energy values E(i) assigned to the first(lowest energy) bin and M is the total number of energy values E(i)calculated for the electrogram segment (i.e., the number of windowsdefined for the electrogram segment). The index v-IEI is the ratio oflow energy windows to all windows in the electrogram segment. The indexv-IEI may be expressed as a percentage by multiplying the result ofequation (2) by 100%. A low percentage v-IEI describes an electrogramsegment with a small percentage of (or no) isoelectric portions, whichmay indicate a high level of fractionation. Conversely, a highpercentage v-IEI describes an electrogram segment with a high percentageof isoelectric portions, which may indicate a low level of (or no)fractionation. The calculated v-IEI may be used on its own as an indexdescribing the amount of fractionation in an electrogram segment. Thev-IEI may be displayed numerically, mapped to a three dimensional map ofthe heart, or presented to a user of the system 8 in any suitablemanner. Additionally, or alternatively, the v-IEI may be combined withone or more other indices to create a composite, or fused, index offractionation.

FIGS. 7 and 8 are example electrogram segments and associated histogramsproduced according to the method described herein. FIG. 7 includes anelectrogram 700 with a one second segment length and a relatively highdegree of fractionation. A 10 ms activity interval was selected, and theenergy level E(i) was calculated for each window on the electrogram 700.The calculated energy levels were assigned to about twenty bins, asshown in the histogram 702. The v-IEI calculated for the electrogram 700was 0.0693 (or 6.93%). FIG. 8 includes an electrogram 800 with a onesecond segment length and very little fractionation. A 10 ms activityinterval was selected, and the energy level E(i) was calculated for eachwindow on the electrogram 800. The calculated energy levels wereassigned to about sixty bins (some bins contain no energy levels), asshown in the histogram 802. The v-IEI calculated for the electrogram 800was 0.8624 (or 86.24%).

As mentioned above, in some embodiments the number of bins to which theenergy levels E(i) are assigned is determined by the system 8 (and moreparticularly, by the computing device 20). The number of bins is foundusing an adaptive binning method to determine a number of bins thatproduces a lowest energy bin that accurately collects windows includingisoelectric intervals. FIG. 9 is a flow diagram of an exemplary method900 for determining the number of bins for use in a method ofcalculating a voltage-based isoelectric index, such as in the method 400shown in FIG. 4. The method 900 is applied to data collected for aparticular electrogram.

At 902, an initial number of bins (N) is selected for the electrogramsegment. Because the method 900 is an adaptive iterative algorithm, thecloser that the initial number of bins is set to the final number ofbins, the fewer iterations will be needed to reach the final number ofbins. Reducing the number of iterations may reduce the amount ofprocessing needed to be performed and free computational power for otheruses. Setting the initial N too high, however, may prevent the method900 from determining a proper number of bins to achieve the desiredcorrelation between the lowest energy bin and windows containingisoelectric segments. Accordingly, in some embodiments, the initialnumber of bins is selected to be one bin. In other embodiments, theinitial N is selected to be ten bins. In other embodiments, the initialN is selected to be any number greater than one and less than ananticipated final number of bins. The initial number of bins may be userselected and/or a default value may be predefined in computing device20. In an example embodiment, a default value for the initial number ofbins is set as ten bins. In other embodiments, any other suitabledefault value for the initial N may be selected. It should be understoodthat selecting the initial number of bins may includeselecting/accepting a default initial N by not specifying, changing, orselecting a number N.

At 904, N number of bins are created. Each bin has a range of energyvalues as described above. At 906, the energy values calculated for eachwindow defined for the electrogram segment are assigned to the binhaving a range in which its energy value is contained.

The energy midpoint (EMB) of the lowest energy bin is calculated at 908.The energy midpoint is calculated by:

$\begin{matrix}{{EMB} = {E_{\min} + \frac{E_{\max} - E_{\min}}{2N}}} & (3)\end{matrix}$

where E_(min) is the lowest energy value for the electrogram segment,E_(max) is the highest energy value for the electrogram segment, and Nis the number of bins.

At 910, the energy midpoint EMB is compared to a noise level energythreshold E_(n), which will be described in more detail below. If theEMB is less than the noise level energy threshold E_(n), the method 900is completed and the number of bins is set at the current number of binsN. If the EMB is not less than the E_(n), the method 900 continues to912. At 912, the number of bins N is incremented by one and the method900 returns to 904. Thus, the method 900 continues with the number ofbins increasing in each iteration until the EMB is less than the E_(n).

In an exemplary embodiment, the energy noise level threshold E_(n) isdetermined based on a peak-to-peak sensitivity setting of the system 8.The sensitivity setting is typically a user selected value.Alternatively, or additionally, the sensitivity setting may be adefault/preset sensitivity setting. The peak-to-peak sensitivity settingis an voltage amplitude (A) in millivolts. In FIG. 5, for example, thepeak-to-peak sensitivity 512 is defined by thresholds 514. To determinethe threshold E_(n), an energy level is calculated for a hypotheticalsinusoidal noise signal between one half A and negative one half A. Thefrequency of the sinusoidal signal is determined by the frequency of thealternating current (AC) power supplied to the system 8. If the system 8uses 60 Hz AC power, the hypothetical sinusoidal noise signal has afrequency of 60 Hz. When the system 8 is powered by 50 Hz AC power, a 50Hz hypothetical sinusoidal noise signal is used. The energy level iscalculated for one window of the selected activity interval according toequation (1). In other embodiments, any other suitable energy noiselevel threshold E_(n), including a predetermined E_(n) and/or adifferently calculated E_(n), may be used.

As discussed above, the voltage-based isoelectric index v-IEI may beutilized as an index by itself or may be combined with one or more otherindices. A study of the v-IEI index and CFE mean for a large sample(around 50,000) of electrogram segments using scatter plots andcorrelation demonstrated no linear correlation between v-IEI and CFEmean. The v-IEI and CFE mean appear to be relating complimentaryinformation. In one example embodiment, the v-IEI metric is combinedwith the CFE mean to provide a hybrid index for evaluation ofelectrogram segments. Alternatively, the v-IEI index may be combinedwith any other index for evaluation of electrograms including, forexample, CFE StdDev, SCI, ACI, and ICL.

To combine the v-IEI and the CFE mean, the two indices need to have thesame range of values. The v-IEI index ranges between zero and one, whilethe CFE mean ranges from zero to the length of the electrogram segmentin milliseconds. In some embodiments, the maximum segment length, andaccordingly the maximum CFE mean, is 8,000 ms. To map the CFE mean ontoa range from zero to one, a sigmoid function is used. The sigmoidfunction for the conversion is:

$\begin{matrix}{{f(x)} = \frac{1}{1 + e^{({\beta {({x - T})}})}}} & (4)\end{matrix}$

where x is the original CFE mean, β is 0.035, and T is 125 ms. Theparameter T in equation (4) may be treated as a center of interest.Variations around the center of interest will be more pronounced thanvariations farther away from the center of interest. The value of T=125ms corresponds to a suggested CFE mean cutoff point between CFAEelectrograms and non CFAE electrograms. Alternatively, T may have adifferent value to highlight a different range of CFE mean values. Theparameter β governs the slope of the sigmoid function. The larger thevalue of β, the sharper the slope of the sigmoid function will be. Thus,β may be selected based on the range of data in which one is interested.In other embodiments, a different sigmoid function, a linear function,and/or another suitable monotonic function may be used to map the CFEmean and v-IEI to the same range.

After the CFE mean values are mapped to the same range as the v-IEIusing equation (4), the v-IEI and CFE mean values may be combined. Inone example, the v-IEI and CFE mean are combined using an F-measure.With two metrics M₁ and M₂, the F-measure (also referred to as an F1score) is:

$\begin{matrix}{F_{1} = {2\frac{M_{1}M_{2}}{M_{1} + M_{2}}}} & (5)\end{matrix}$

The F-measure produced by combining v-IEI and CFE mean values usingequation (5) assigns equal weight to the v-IEI and CFE mean values. Insome embodiments, the v-IEI values are given more weight in thecombination by using a more general form of the F-measure:

$\begin{matrix}{F_{\alpha} = {\left( {1 + \alpha^{2}} \right)\frac{M_{1}M_{2}}{{\alpha^{2}*M_{1}} + M_{2}}}} & (6)\end{matrix}$

where α is a weighting factor, M₁ is the CFE mean, and M₂ is the v-IEI.The variable a gives different weighting to the harmonics of equation(6). The value of a is positive. If the value of a is 1, equation (6)reduces to equation (5) and M₁ and M₂ receive equal emphasis. If thevalue of a is smaller than 1, M₁ receives more emphasis than M₂. If thevalue of a is greater than 1, more emphasis is placed on M₂ than M₁. Inan example embodiment, equation (6) was used to combine v-IEI and CFEmean values using α=1.0954. This value of a resulted from analysis ofexperimental results from application of the techniques described hereinto an existing dataset of electrograms. Thus, slightly more weight wasapplied to the v-IEI values in the combination. In other embodiments anyother suitable value may be used for a.

The methods described herein were applied to several sample electrogramsto determine the v-IEI. The determined the v-IEI and CFE mean werecombined using equations (4) and (6), with α=1.0954 in equation (6). Thev-IEI, CFE mean, and combined metric were evaluated for agreement withthe expert annotations of the sample electrograms. A receiver operatingcharacteristic (ROC) analysis was used to compare the v-IEI, CFE mean,and combined metric. FIG. 10 is a graph 1000 summarizing the results. Inthe graph, trace 1002 is the CFE mean, trace 1004 is the v-IEI, and 1006is the F-measure combined CFE mean and v-IEI. The graph 100 generallyshows greater sensitivity and specificity achieved using the F-measurethan is achieved using the CFE mean alone.

For v-IEI mapping, in one embodiment, two parameters may be set: arefractory parameter and a floor parameter. The refractory parameterdefines a minimum segment length considered to be iso-electric. Forexample, the refractory parameter may be in a range from 30 milliseconds(ms) to 60 ms. The floor parameter determines a maximum peak to peakvoltage to be considered iso-electric. For example, the floor parametermay be in a range from 0.03 millivolts (mV) to 0.05 mV, or above a noisefloor. In one embodiment, when a user adjusts at least one of therefractory parameter and the floor parameter, an v-IEI map is recomputedand redisplayed. An overall segment length (e.g., segment length 502(shown in FIG. 5)) is also specified. The overall segment length may be,for example, in a range from 1 second (s) to 8 s.

The relationship between v-IEI and CFE has been experimentallydemonstrated. For example, for an overall segment length of one second,the following Table 1 lists experimental data obtained for a pluralityof electrograms:

TABLE 1 CFE <120 ms v-IEI <25% v-IEI <20% % of all Electrograms 24.70 ±11.28%  9.16 ± 6.75% 6.9 ± 5.3% % of Electrograms with 100% 98.42 ±1.40% 98.64 ± 1.46%  CFE <120 ms % of Electrograms with 32.58 ± 14.07%100% 100% v-IEI <25%

In Table 1, the first row of the table (i.e., “% of all Electrograms”)represents the percentage of all acquired electrograms that fit thecriterion of the respective columns. For example, 24.70% of all acquiredelectrograms had a CFE mean of less than 120 ms. The second and thirdrows represent the additional criterion for the column electrograms. Forexample, 98.42% of the electrograms that had a v-IEI index under 25%were also electrograms with a CFE mean of less than 120 ms. Further,32.58% of electrograms with a CFE mean of less than 120 ms, had a v-IEIindex under 25%. Accordingly, from the experimental data of Table 1, itis apparent that the CFE mean and v-IEI index are related to oneanother. Other, similar experiments were also conducted with similarresults.

Although certain embodiments of this disclosure have been describedabove with a certain degree of particularity, those skilled in the artcould make numerous alterations to the disclosed embodiments withoutdeparting from the spirit or scope of this disclosure. All directionalreferences (e.g., upper, lower, upward, downward, left, right, leftward,rightward, top, bottom, above, below, vertical, horizontal, clockwise,and counterclockwise) are only used for identification purposes to aidthe reader's understanding of the present disclosure, and do not createlimitations, particularly as to the position, orientation, or use of thedisclosure. Joinder references (e.g., attached, coupled, connected, andthe like) are to be construed broadly and may include intermediatemembers between a connection of elements and relative movement betweenelements. As such, joinder references do not necessarily infer that twoelements are directly connected and in fixed relation to each other. Itis intended that all matter contained in the above description or shownin the accompanying drawings shall be interpreted as illustrative onlyand not limiting. Changes in detail or structure may be made withoutdeparting from the spirit of the disclosure as defined in the appendedclaims.

When introducing elements of the present disclosure or the variousversions, embodiment(s) or aspects thereof, the articles “a”, “an”,“the” and “said” are intended to mean that there are one or more of theelements. The terms “comprising”, “including” and “having” are intendedto be inclusive and mean that there may be additional elements otherthan the listed elements. The use of terms indicating a particularorientation (e.g., “top”, “bottom”, “side”, etc.) is for convenience ofdescription and does not require any particular orientation of the itemdescribed.

As various changes could be made in the above without departing from thescope of the disclosure, it is intended that all matter contained in theabove description and shown in the accompanying drawings shall beinterpreted as illustrative and not in a limiting sense.

What is claimed is:
 1. A computer implemented method for evaluating anelectrogram containing a plurality of data samples each having avoltage, the computer implemented method comprising: defining aplurality of windows, each window of the plurality of windows being alength of time defined by an activity interval; calculating respectiveenergy levels for each window of the plurality of windows of theelectrogram; defining a first threshold and a second threshold;determining a first count of the respective energy levels andcorresponding windows below the first threshold; determining a secondcount of the respective energy levels and corresponding windows abovethe second threshold; calculating a value based on a ratio of a sum ofthe first count and the second count to a total number of the respectiveenergy levels; mapping the value to a three dimensional map of a heart;and presenting the map to a user on a display to indicate fractionationof the electrogram.
 2. The computer implemented method of claim 1,wherein calculating the respective energy levels is based on a summationof absolute values of voltages of each data sample in each window. 3.The computer implemented method of claim 1, wherein defining theplurality of windows comprises defining one window for each sample ofdata in the electrogram.
 4. The computer implemented method of claim 1,wherein the first threshold is a low energy level threshold and thesecond threshold is a high energy level threshold.
 5. The computerimplemented method of claim 1, further comprising generating a histogramof the sum of the first count and the second count.
 6. The computerimplemented method of claim 1, wherein calculating the value comprisescomputing an isoelectric index based on respective voltages of theplurality of data samples.
 7. The computer implemented method of claim6, wherein calculating the value further comprises combining a firstcalculated index with a second calculated index for the electrogram toproduce a fused index.
 8. The computer implemented method of claim 7,wherein the second calculated index is a complex fractionatedelectrogram (CFE) mean, and combining the first calculated index withthe CFE mean comprises mapping the CFE mean to a same range of values asthe first calculated index using a sigmoid function and combining thefirst calculated index with the mapped CFE mean using an F-measurefunction.
 9. The computer implemented method of claim 1 furthercomprising receiving the activity interval for the electrogram from auser.
 10. The computer implemented method of claim 1 further comprisingdetermining the activity interval for the electrogram.
 11. A system forevaluating an electrogram containing a plurality of data samples eachhaving a voltage, the system comprising: a computing device configuredto receive the data samples, the computing device comprising: aprocessor; a display coupled to said processor; and at least one memorydevice coupled to said processor, the memory device storingcomputer-executable instructions that, when executed by the processor,cause the computing device to: determine an activity interval for theelectrogram; define a plurality of windows, each window of the pluralityof windows being a length of time defined by the activity interval;calculate respective energy levels for each window of the plurality ofwindows of the electrogram; define a first threshold and a secondthreshold; determine a first count of the respective energy levels andcorresponding windows below the first threshold; determine a secondcount of the respective energy levels and corresponding windows abovethe second threshold; calculate a value based on a ratio of a sum of thefirst count and the second count to a total number of the respectiveenergy levels; map the value to a three dimensional map of a heart; andpresent the map to a user on a display to indicate fractionation of theelectrogram.
 12. The system of claim 11, wherein the memory devicefurther stores computer-executable instructions that, when executed bythe processor, cause the computing device to define one window for eachsample of data in the electrogram.
 13. The system of claim 11, whereinthe memory device further stores computer-executable instructions that,when executed by the processor, cause the computing device to calculatethe energy level for each window by summing an absolute value of avoltage of each data sample in the window.
 14. The system of claim 11,wherein the first threshold is a low energy level threshold and thesecond threshold is a high energy level threshold.
 15. The system ofclaim 11, wherein the memory device further stores computer-executableinstructions that, when executed by the processor, cause the computingdevice to receive a first user input value for the first threshold and asecond user input value for the second threshold.
 16. The system ofclaim 11, further comprising a display device, wherein the memory devicefurther stores computer-executable instructions that, when executed bythe processor, cause the computing device to generate a histogram of thesum of the first count and the second count.
 17. The system of claim 11,wherein the memory device further stores computer-executableinstructions that, when executed by the processor, cause the computingdevice to calculate the value by computing an isoelectric index based onrespective voltages of the data sample.
 18. The system of claim 17,wherein the memory device further stores computer-executableinstructions that, when executed by the processor, cause the computingdevice to calculate the value by combining a first calculated index witha second calculated index for the electrogram to produce a fused index.19. The system of claim 18, wherein the second calculated index is acomplex fractionated electrogram (CFE) mean, and wherein the memorydevice further stores computer-executable instructions that, whenexecuted by the processor, cause the computing device to map the CFEmean to a same range of values as the first calculated index using asigmoid function and combine the first calculated index with the mappedCFE mean using an F-measure function.
 20. The system of claim 11,wherein the memory device further stores computer-executableinstructions that, when executed by the processor, cause the computingdevice to receive a user value for the activity interval for theelectrogram.